DeepRL-TensorFlow2
TensorFlow2.0-for-Deep-Reinforcement-Learning
| DeepRL-TensorFlow2 | TensorFlow2.0-for-Deep-Reinforcement-Learning | |
|---|---|---|
| 2 | 1 | |
| 573 | 81 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | 8 months ago | |
| Python | Python | |
| Apache License 2.0 | - |
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DeepRL-TensorFlow2
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PPO implementation in TensorFlow2
I've been searching for a clean, good, and understandable implementation of PPO for continuous action space with TF2 witch is understandable enough for me to apply my modifications, but the closest thing that I have found is this code which seems to not work properly even on a simple gym cartpole env (discussed issues in git-hub repo suggest the same problem) so I have some doubts :). I was wondering whether you could recommend an implementation that you trust and suggest :)
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Question about using tf.stop_gradient in separate Actor-Critic networks for A2C implementation for TF2
I have been looking at this implementation of A2C. Here the author of the code uses stop_gradient only on the critic network at L90 bur not in the actor network L61 for the continuous case. However , it is used both in actor and critic networks for the discrete case. Can someone explain me why?
TensorFlow2.0-for-Deep-Reinforcement-Learning
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Beginner attempting to implement Noisy DQN
I forgot to say that I'm using tensorflow, nevertheless I managed to find a git implementation for tensorflow 2 of the noisy dense layer (https://github.com/Huixxi/TensorFlow2.0-for-Deep-Reinforcement-Learning/blob/master/07_noisynet.py) and tried to adapt it to my needs.
What are some alternatives?
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
ydata-synthetic - Synthetic data generators for tabular and time-series data
trax - Trax — Deep Learning with Clear Code and Speed
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
Deep-Reinforcement-Learning-Hands-On - Hands-on Deep Reinforcement Learning, published by Packt
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving